Improved Multi-Verse Optimizer In Text Document Clustering For Topic Extraction
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Date
2021-02
Authors
Abasi, Ammar Kamal Mousa
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
This study aims to propose a suitable TE approach, which provides a better overview of the text documents. To achieve this aim: First, A new feature selection method for TDC, that is, binary multi-verse optimizer algorithm (BMVO) is proposed to eliminate irrelevantly, redundant features and obtain a new subset of more
informative features. Second, three multi-verse optimizer algorithm (MVOs), namely, basic MVO, modified MVO, hybrid MVO is proposed to solve the TDC problem; these algorithms are incremental improvements of the preceding versions. Third, a novel ensemble method for an automatic TE from a collection of text document is proposed to extract the topics from the clustered documents.
Description
Keywords
Improved Multi-Verse Optimizer , Text Document Clustering , Topic Extraction